Semiconductor manufacturing system, behavior recognition device and semiconductor manufacturing method

ABSTRACT

A behavior recognition device for recognizing behaviors of a semiconductor manufacturing apparatus includes a storage device and a control unit. The storage device is configured to store log data of the semiconductor manufacturing apparatus. The control unit is cooperatively connected to the storage device, and configured to build a transition state model based on the log data to analyze behaviors related to wafer transfer sequences and manufacturing operations of the semiconductor manufacturing apparatus.

BACKGROUND

The manufacturing of semiconductor devices involves many operations,including deposition, photolithography, etching, and the like. Each ofthe above operations may include several different sub-operations, andthese sub-operations may be performed in a multi-chamber type clustersemiconductor manufacturing apparatus. The multi-chamber type clustersemiconductor manufacturing apparatus may be used to deal with aplurality of wafers at the same time, thereby increasing throughput. Themulti-chamber type cluster semiconductor manufacturing apparatusincludes a series of heterogeneous manufacturing units for performingdifferent operations or sub-operations, and the operation of themulti-chamber type cluster semiconductor manufacturing apparatus is verycomplex. Thus, it is difficult to capture the root cause of productivityloss.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the embodiments of the present disclosure are readilyunderstood from the following detailed description when read with theaccompanying figures. It is noted that, in accordance with the standardpractice in the industry, various structures are not drawn to scale. Infact, the dimensions of the various structures may be arbitrarilyincreased or reduced for clarity of discussion.

FIG. 1 is a schematic view diagram illustrating a multi-chamber typecluster semiconductor manufacturing apparatus in accordance with someembodiments of the present disclosure.

FIG. 2 is a schematic view diagram illustrating a semiconductormanufacturing system in accordance with some embodiments of the presentdisclosure.

FIG. 3A is a schematic diagram of a machine learning model in accordancewith some embodiments of the present disclosure.

FIG. 3B is a schematic diagram illustrating transition states of themachine learning model of FIG. 3A in accordance with some embodiments ofthe present disclosure.

FIG. 4 is a schematic diagram illustrating the relation between good/badsequences and normal/abnormal sequences, and the relation betweengood/bad activities and normal/abnormal activities.

FIG. 5 is a schematic diagram illustrating job execution order of an ATMrobot between the load ports and load locks in accordance with someembodiments of the present disclosure.

FIG. 6 is a schematic diagram illustrating job execution order of a loadlock in accordance with some embodiments of the present disclosure.

FIG. 7 is a flow chart illustrating a semiconductor manufacturing methodin accordance with various aspects of one or more embodiments of thepresent disclosure.

FIG. 8 is a schematic diagram illustrating a scheme of a simulator inaccordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION

The following disclosure provides many different embodiments, orexamples, for implementing different features of the provided subjectmatter. Specific examples of elements and arrangements are describedbelow to simplify the present disclosure. These are, of course, merelyexamples and are not intended to be limiting. For example, the formationof a first feature over or on a second feature in the description thatfollows may include embodiments in which the first and second featuresare formed in direct contact, and may also include embodiments in whichadditional features may be formed between the first and second features,such that the first and second features may not be in direct contact. Inaddition, the present disclosure may repeat reference numerals and/orletters in the various examples. This repetition is for the purpose ofsimplicity and clarity and does not in itself dictate a relationshipbetween the various embodiments and/or configurations discussed.

Further, spatially relative terms, such as “beneath,” “below,” “lower,”“above,” “over,” “upper,” “on,” and the like, may be used herein forease of description to describe one element or feature's relationship toanother element(s) or feature(s) as illustrated in the figures. Thespatially relative terms are intended to encompass differentorientations of the device in use or operation in addition to theorientation depicted in the figures. The apparatus may be otherwiseoriented (rotated 90 degrees or at other orientations) and the spatiallyrelative descriptors used herein may likewise be interpretedaccordingly.

As used herein, the terms such as “first,” “second” and “third” describevarious elements, components, regions, layers and/or sections, theseelements, components, regions, layers and/or sections should not belimited by these terms. These terms may be only used to distinguish oneelement, component, region, layer or section from another. The termssuch as “first,” “second” and “third” when used herein do not imply asequence or order unless clearly indicated by the context.

As used herein, the terms “approximately,” “substantially,”“substantial” and “about” are used to describe and account for smallvariations. When used in conjunction with an event or circumstance, theterms can refer to instances in which the event or circumstance occursprecisely as well as instances in which the event or circumstance occursto a close approximation.

Refer to FIG. 1. FIG. 1 is a schematic view diagram illustrating amulti-chamber type cluster semiconductor manufacturing apparatus inaccordance with some embodiments of the present disclosure. As shown inFIG. 1, the multi-chamber type cluster semiconductor manufacturingapparatus 1 may include a series of heterogeneous manufacturing unitsconfigured to perform different operations or implement differentfunctions. In some embodiments, the manufacturing units may include loadports (e.g., FOUP) 10, ATM robots F1, F2 , an aligner X, load locks L1,L2, VTM robots V1, V2, first processing chambers such as chambers A1,A2, and second processing chambers such as chambers B1, B2. The loadports 10 are configured to place cassettes for storing wafers. The ATMrobots F1, F2 are configured to transfer wafers between the load ports10 and the load locks L1, L2 in an atmospheric environment, The VTMrobots V1, V2 are configured to transfer the wafer in the vacuumenvironment. The wafer is transferred to the aligner X for calibratingthe orientation of the wafer before transferring to the load lock L1 orL2. The load locks L1, L2 are spaces configured to load-in and load-outthe wafer. When the wafer is load-in, a front door of the load lock L1or L2 opens to receive the wafer from the ATM robot F1 or F2. The frontdoor is then closed, and the load lock L1 or L2 is pumped to a vacuumenvironment. A back door of the load lock L1 or L2 then opens, and thewafer is transferred to the chamber A1 or A2 by the VTM robot V1 or V2.When the wafer is load-out, the back door of the load lock L1 or L2opens to receive the wafer from the VTM robot V1 or V2. The back door ofthe load lock L1 or L2 is then closed, and the load lock L1 or L2 isventilated to an atmospheric environment. The front door of the loadlock L1 or L2 then opens, and the wafer is transferred to the load port10 by the ATM robot F1 or F2. In some embodiments, the first processingchambers A1, A2 and the second processing chambers B1, B2 includedifferent types of processing chambers for performing differentoperations, and may be in communication with a center chamber 12.

In some embodiments, the multi-chamber type cluster semiconductormanufacturing apparatus 1 may include but is not limited to, for examplea deposition apparatus such as a physical vapor deposition (PVD)apparatus. The first processing chambers A1, A2 may be configured topre-heat the wafers. The second processing chambers B1, B2 may beconfigured to perform PVD operation on the wafers. In some otherembodiments, the multi-chamber type cluster semiconductor manufacturingapparatus 1 may include a chemical vapor deposition (CVD) apparatus,etching apparatus, a photolithography apparatus or the like.

As shown in FIG. 1, the multi-chamber type cluster semiconductormanufacturing apparatus 1 can perform manufacturing operations onmultiple wafers simultaneously and/or successively. At a time point,some wafers may be processed in the first processing chambers A1, A2 andthe second processing chambers B1, B2, some wafers may be handled by theATM robots F1, F2 and the VTM robots V1, V2, some wafers may be in theload locks L1 and L2, and some wafers may be waiting to load-in. Themanufacturing operations of the multi-chamber type cluster semiconductormanufacturing apparatus 1 are complex and continuously performed. Onceone of the manufacturing operations for a wafer is idled, it may affectthe manufacturing operations of other wafers, and it is very difficultto capture the root cause of the idle.

In one or more embodiments of the present disclosure, a behaviorrecognition device cooperatively connected to one or more semiconductormanufacturing apparatuses is used to process log data of the one or moresemiconductor manufacturing apparatuses, and to analyze behaviorsrelated to manufacturing operations of manufacturing units of the one ormore semiconductor manufacturing apparatuses. The behavior recognitiondevice can real-time receive the log data the one or more semiconductormanufacturing apparatuses as long as they are generated during themanufacturing operations are performed.

The behavior recognition device can real-time process the log data, andautomatically build a machine learning model such as a transition statemodel. The machine learning model can automatically recognize thebehaviors of all the manufacturing units, and generate behaviorattributions for each wafer. Accordingly, the machine learning model canidentify good/bad behaviors and capture the root cause of the badbehaviors with high accuracy. That is, behavior attributions can belearned.

As used herein, the good/bad behaviors may be identified in terms ofproductivity. The good/bad behaviors may include good/bad sequences andgood/bad activities. A good sequence refers to a wafer transfer sequencehaving higher transition probability and/or standard loop of the wafertransfer sequences, while a bad sequence refers to a wafer transfersequence having lower transition probability and/or redundant loop. Agood activity refers to an activity of a manufacturing unit having lowertime consumption and/or consistent with the activities of othermanufacturing units, while a bad activity refers to an activity of amanufacturing unit having higher time consumption and/or inconsistentwith the activities of other manufacturing units.

The behavior recognition device can further perform a simulation basedon the transition state model in some embodiments, the transition statemodel can be output to a simulator, ail the behaviors including the goodbehaviors and bad behaviors can be reproduced in the simulator, and themanufacturing operations in the transition state model can be simulatedby adjusting the control rule of the bad behavior. When the simulationresult shows that adjusting the control rule can fix the bad behavior,the adjusted control rule can be adopted to perform the manufacturingoperations on a new batch of wafers with the adjusted control rule.

FIG. 2 is a schematic view diagram illustrating a semiconductormanufacturing system in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2, the semiconductor manufacturing system50 may include at least one semiconductor manufacturing apparatus 1, anda behavior recognition device. The semiconductor manufacturing apparatus1 may include a multi-chamber type cluster semiconductor manufacturingapparatus 1 as illustrated in FIG. 1. The behavior recognition device 2is cooperatively connected to the semiconductor manufacturing apparatus1. As shown in FIG. 2, the behavior recognition device 2 iscooperatively connected to two semiconductor manufacturing apparatuses 1for example. The number of the semiconductor manufacturing apparatus 1is not limited.

In addition to the manufacturing units as illustrated in FIG. 1, thesemiconductor manufacturing system 50 may include a first control unit34 and a first storage device 32. The first control unit 34 isconfigured to control the manufacturing operations of the series ofmanufacturing units based on the control rules. In some embodiments, thecontrol rules may include the job handling sequences of themanufacturing units, the wafer transfer sequence, operation parameters,etc. The first control unit 34 is also configured to generate log datarecording the manufacturing operations of the series of manufacturingunits. In some embodiments, the control rules of the semiconductormanufacturing system 50 are set by vendors, and may not be all known tothe manufacturers. The log data, however, may record every manufacturingoperation such as what each manufacturing unit does and how long eachmanufacturing operation lasts in terms of time. The first storage device32 is cooperatively connected to the first control unit 34 andconfigured to store the log data transferred from the first control unit34.

The behavior recognition device 2 includes a second storage device 42,and a second control unit 44. The second storage device 42 iscooperatively connected to the first control unit 34 of thesemiconductor manufacturing apparatus 1, and configured to store the logdata transferred from the first control unit 34. In some embodiments,the first control unit 34 not only transfers the log data to the firststorage unit 32 of the semiconductor manufacturing apparatus 1, but alsoduplicates the log data and transfers to the second storage device 42 ofthe behavior recognition device 2. The first control unit 34 cantransfer the log data to the second storage device 42 of the behaviorrecognition device 2 and to the first storage device 32 of thesemiconductor manufacturing apparatus 1 in a real-time manner as the logdata is generated, or in a postponed manner. The second control unit 44is cooperatively connected to the second storage device 42, andconfigured to receive the log data from the second storage device 42.The second control unit 44 can also build a transition state model toanalyze behaviors related to the manufacturing operations of the seriesof manufacturing units based on the log data.

In some embodiments, the first control unit 34 and the second controlunit 44 are two micro control units (MCUs), and each may include aprocessor such as a central processing unit (CPU). The first controlunit 34 and the second control unit 44 each may further include embeddedmemory for storing instructions. In some embodiments, the first storagedevice 32 and the second storage device 42 are two storage devices suchas hard disks or the like. In some embodiments, the second storagedevice 42 of the behavior recognition device 2 may include a networkattached storage (NAS). The second storage device 42 may be connected tothe semiconductor manufacturing apparatus 1 in a wired manner or awireless manner.

In some embodiments, instead of processing the log data in the firststorage device 32, the second control unit 44 processes the log data inthe second storage unit 42. This would minimize the work load of thefirst control unit 34 and the first storage device 32, and thus wouldreduce the risk of overload occurring in the semiconductor manufacturingapparatus 1. In some other embodiments, the first control unit 34 andthe second control unit 44 may be a control unit and/or the firststorage device 32 and the second storage device 42 may be a storagedevice, as long as the computing and data accessing abilities are highenough to perform both the manufacturing operations and the analysis ofthe log data at the same time.

In some embodiments, the behavior recognition device 2 may furtherinclude a data transmission unit 46 cooperatively connected to thesecond storage device 42 and the second control unit 44. The datatransmission unit 46 may include an interface configured to transfer thelog data of the semiconductor manufacturing apparatus 1 from the secondstorage device 42 to the second control unit 44 in a real-time manner orin a postponed manner.

In some embodiments, the log data of different manufacturing unitsand/or different semiconductor manufacturing apparatuses 1 may berecorded in different formats. The second control unit 44 of thebehavior recognition device 2 may further include a parser 48 configuredto convert the log data recording the manufacturing operations of themanufacturing units in different formats into a uniform format as aninput of the second control unit 44. In some embodiments, the parser 48can be implemented by a hardware device. Additionally or alternatively,the parser can be implemented by software or firmware.

Referring to FIG. 3A. FIG. 3A is a schematic diagram of a machinelearning model in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3A, the machine learning model includesmultiple tiers configured to execute different learning procedures. Themachine learning model may adopt Neutral Network based algorithm. Insome embodiments, the machine learning model includes a first tier (Tier1), a second tier (Tier 2) and a third tier (Tier 3). Tier 1 may beconfigured to perform a unit correlation learning from the perspectiveof each wafer (Wafer view). In Tier 1, wafer transfer sequences and timeconsumption for each of the wafers in the manufacturing units based onthe log data are learned to construct a transition state model of wafertransfer sequences. Tier 2 may be configured to learn the activity foreach manufacturing unit from the perspective of each manufacturing unit(Unit view). In tier 1, normal sequence and abnormal sequence can beidentified based on the transition probabilities and/or loops of thewafer transfer sequences.

In Tier 2, the manufacturing operations of each manufacturing unitbefore and after arrivals of the wafers are learned to construct atransition state model of manufacturing unit operations. For example,the manufacturing operations of each of the manufacturing units such asATM robot F1 or F2, VTM robot V1 or V2, first processing chamber A1 orA2, second processing chamber B1 or B2, load lock L1 or L2 are learned.In Tier 2, the mapping between the wafer transfer sequence and themanufacturing operations of the manufacturing units may also be learned.In Tier 2, the manufacturing cycles of the manufacturing operations ofthe manufacturing units and a time consumption of each activity in themanufacturing cycles can be learned, and normal activity and abnormalactivity can be identified based on the time consumption and themanufacturing cycles.

Tier 3 may be configured to analyze behaviors related to themanufacturing operations of the series of manufacturing units. In tier3, good/bad sequences can be identified from the normal/abnormalsequences, and good/bad activities can be identified from thenormal/abnormal activities in terms of productivity. For example, badsequence or bad activity can be confirmed in case this sequence oractivity results in productivity loss compared to other sequences oractivities. Furthermore, tier 3 may further learn behavior attributionsfor the bad behaviors, and to capture a root cause of the bad behaviorbased on the behavior attributions. Tier 3 may be configured to learncontrol rules of the good behaviors and the bad behavior to construct atransition state model by cross learning between Tier 1 and Tier 2 afterTier 1 and Tier 2 trained a plurality of wafers. In some embodiments,the transition state models constructed in Tier 1, Tier 2 and/or Tier 3may include but are not limited to, for example Markov chain model.

As shown in FIG. 3A, the machine learning model is constructed bymultiple learning planes (e.g., Tier 1 and Tier 2), and one crosslearning plane (e.g., Tier 3) so that the machine learning model mayalso be referred to as a 3D self-expansive cascade machine learningmodel.

Refer to FIG. 3B. FIG. 3B is a schematic diagram illustrating transitionstates of the machine learning model of FIG. 3A in accordance with someembodiments of the present disclosure. In Tier 1, wafer transfersequences and time consumption for each of the wafers in themanufacturing units based on the log data are learned to construct atransition state model of wafer transfer sequences. For example, thewafer transfer sequences among the ATM robot F1, the load locks L1 andL2 and the VTM robot V1, and the time consumption of the wafer among theabove manufacturing units are learned from the log data to construct atransition state model such as Markov chain model. In the Markov chainmodel, each location of the wafer represents a state, and theprobability of the Markov process changing from one state to anotherstate (indicated by an arrow) can be learned from the log data.

In Tier 2, the activities of each manufacturing operation for eachmanufacturing unit are learned to construct another transition statemodel. For example, the activities of the load lock L2 are learned fromthe log data. The load lock L2 may be in state a in which the load lockL2 is in atmospheric state, and several activities may be executed whenthe load lock L2 in state a. The load lock L2 may be transitioned fromstate a to state a1 in which the front door of the load lock L2 is opento send a wafer. The load lock L2 may be transitioned from state a tostate a2 in which the front door of the load lock L2 is open to receivea wafer. The load lock L2 may be transitioned from state a to state p inwhich the load lock L2 is pumped. The load lock L2 may be transitionedfrom state p to state v in which the load lock L2 is vacuumed. The loadlock L2 may be transitioned from state v to state v1 in which the backdoor of the load lock L2 is open to send a wafer. The load lock L2 maybe transitioned from state v to state v2 in which the back door of theload lock L2 is open to receive a wafer. The load lock L2 may betransitioned from state v to state d in which the load lock L2 isventilated. The load lock L2 may be transitioned from state d to state ain which the load lock L2 is in atmospheric state. In some embodiments,the load lock L2 may be transitioned from state p to state s in whichthe pressure in the load lock L2 is unstable based on the log data. Thestate s may be identified as an abnormal behavior based on theoccurrence probability and/or time consumption from the log data.Similar to the activities of the load lock L2, the activities of othermanufacturing units can be learned to construct a transition statemodel.

In Tier 3, the transition state models of Tier 1 and Tier 2 are crossreferred to construct a transition model to identify the good/badsequences from the normal/abnormal sequences learned from tier 1, and toidentify the good/bad activities from the normal/abnormal activitiesfrom tier 2. Tier 3 can also learn the control rules including jobhandling sequences of the manufacturing units, operation parameters,time consumption, etc.

FIG. 4 is a schematic diagram illustrating the relation between good/badsequences and normal/abnormal sequences, and the relation betweengood/bad activities and normal/abnormal activities. As shown in FIG. 4,the bad sequence identified in tier 3 may be identified as either anormal sequence or an abnormal sequence in tier 1, and the bad activityidentified in tier 3 may be identified as either a normal activity or anabnormal activity in tier 2.

The machine learning model may output the control rules of themanufacturing units of the semiconductor manufacturing apparatus basedon the log data, and behaviors of the manufacturing units can beevaluated to find the root cause of an abnormal behavior.

Refer to FIG. 5. FIG. 5 is a schematic diagram illustrating jobexecution order of an ATM robot between the load ports (e.g., FOUP) andload locks in accordance with some embodiments of the presentdisclosure. As shown in FIG. 5, Job 1 is an activity that transfers awafer W1 from load port 10A to load lock L1 by an ATM robot Fl, and Job2 is an activity that transfers a wafer W2 from load lock L2 to loadport 10B by an ATM robot F1. In some embodiments, the wafer W1 may bealigned by an aligner X in Job 1, and the wafer W2 may be cooled in acool station Y in Job 2. In some embodiments, the default location ofthe ATM robot F1 is set to be closer to the load ports 10A, 10B.

Table 1 lists the control rules of Job 1 and Job 2 and waste time indifferent job order based on the output of the machine learning modellearned from the log data.

TABLE 1 Job queue Job order Control rules Waste time 1 Job 1 Job 1arrives; Job 2 does not arrive   0 second 1 Job 1 Job 2 arrives; Job 1does not arrive 0.3 seconds 2 Job 2 Job 2 arrival is ahead of Job 1 6.7seconds shorter than or equal to 6 seconds 2 Job 2 Job 2 arrival isahead of Job 1 0.4 seconds longer than 6 seconds

As shown in Table 1, the control rule shows the higher priority of Job 1causes the 6.7 seconds of waste time. Accordingly, this control rule maybe reconsidered to reduce the waste time.

Refer to FIG. 6. FIG. 6 is a schematic diagram illustrating jobexecution order of a load lock in accordance with some embodiments ofthe present disclosure. The load lock L may include a stack load lockwhich can accommodate two wafers W3 and W4. As shown in FIG. 6, wafer W3is an un-processed wafer to be transferred to the main frame such as thecenter chamber 12 of the semiconductor manufacturing apparatus, andwafer W4 is a processed wafer to be transferred from the center chamber12 to the load port 10. The load lock L is in a vacuum state, and wafersW3 and W4 are in the load lock L. Job 1 is an activity that keeps theload lock L vacuumed, and transfer to an available manufacturing unitwhen available. Job 2 is an activity that ventilates the load lock L toan atmospheric state, and transfer wafer W4 to the load port 10.

Table 2 lists the control rules of Job 1 and Job 2 and waste time indifferent job order based on the output of the machine learning modellearned from the log data.

TABLE 2 Job queue Job order Control rules Waste time 1 Wafer W3 > WaferW4 Wafer 4 does not arrive   0 second 2 Wafer W4 > Wafer W3 Wafer W4arrives 17.4 seconds

As shown in Table 2, the control rule shows the control unit will switchthe load lock L from the atmospheric state to the vacuumed state whenany processed wafer arrives, which cause 17.4 seconds of waste time.Accordingly, this control rule may be reconsidered to reduce the wastetime.

In some embodiments of the present disclosure, the behavior recognitiondevice cooperatively connected to the semiconductor manufacturingapparatus can construct a transition state model based on the log datato automatically recognize the behaviors including the sequences of allthe wafers and the activities of all the manufacturing units, andgenerate behavior attributions for each wafer. Accordingly, the machinelearning model can identify good/bad sequences of the wafers andgood/bad activities of the manufacturing units, and capture the rootcause(s) of the bad behaviors with high accuracy. The machine learningmodel can further learn the control rules related to the bad behaviorsand the control rules related to the bad behavior, thereby capturing theroot cause.

Refer to FIG. 7. FIG. 7 is a flow chart illustrating a semiconductormanufacturing method in accordance with various aspects of one or moreembodiments of the present disclosure. The method 200 may proceed withoperation 210 followed by method 100 in which a transition state modelof all manufacturing units is output to a simulator. The method 200proceeds with operation 212 in which the bad behavior is reproducedbased on the transition state model in the simulator. The method 200proceeds with operation 214 in which the solution to the bad behavior isfound by changing the transition state model. The method 200 proceedswith operation 216 in which the manufacturing operations of themanufacturing units are performed on a plurality of wafers with theadjusted control rule based on a result of the simulation.

The method 200 is merely an embodiment, and is not intended to limit thepresent disclosure beyond what is explicitly recited in the claims.Additional operations can be provided before, during, and after themethod 200, and some operations described can be replaced, eliminated,or moved around for additional embodiments of the method.

The behavior recognition device can further perform a simulation basedon the transition state model. In some embodiments, the transition statemodel can be output to a simulator, all the behaviors including theabnormal behavior can be reproduced in the simulator, and themanufacturing operations in the transition state model can be simulatedby adjusting the control rule of the abnormal behavior. When thesimulation result shows that adjusting the control rule can fix theabnormal behavior, the adjusted control rule can be adopted to performthe manufacturing operations on a new batch of wafers with the adjustedcontrol rule.

FIG. 8 is a schematic diagram illustrating a scheme of a simulator inaccordance with some embodiments of the present disclosure. As shown inFIG. 8, the transition state model constructed based on the log data canbe output to a simulator 60. In some embodiments, the manufacturingoperations, optional parameters and control rules of the manufacturingunits of the semiconductor manufacturing apparatus can be stored using auniversal tool command language (TCL) application or interface, andconfigured as role scripts. The simulator 60 may include control unit toperform the simulation, and storage device to store the transition statemodel. In some embodiments, the control unit and/or the storage deviceof the simulator 60 may be different from that of the behaviorrecognition device. In some other embodiments, the simulator 60 and thebehavior recognition device may share common control unit and/or storagedevice. A simulation can be executed by an executor of the simulator 60to reproduce the behavior of the manufacturing units of thesemiconductor manufacturing apparatus. The reproduction of the behaviorof the manufacturing units of the semiconductor manufacturing apparatuscan be configured to calibrate the simulator 60. When the simulationresult is similar to the manufacturing operation of the manufacturingunits, the manufacturing operations can be simulated in the transitionstate model by adjusting the control rule of the manufacturing units.When the adjusted control rule improves the performance (e.g., reductionof waste time) in the simulation result, the adjusted control rule canhe adopted to perform the manufacturing operations of the series ofmanufacturing units on a plurality of wafers in real manufacturing.

In some embodiments of the present disclosure, a behavior recognitiondevice cooperatively connected to one or more semiconductormanufacturing apparatuses is used to process log data of the one or moresemiconductor manufacturing apparatuses, and to analyze behaviorsrelated to wafer transfer sequences and manufacturing operations ofmanufacturing units of the one or more semiconductor manufacturingapparatuses. The behavior recognition device can automatically build amachine learning model such as a transition state model. The machinelearning model can automatically recognize the behaviors of all thewafers and the manufacturing units, and generate behavior attributionsfor each wafer and each activity of each manufacturing unit.Accordingly, the machine learning model can identify good behaviors andbad behaviors of the manufacturing units in terms of productivity loss,capture the root cause(s) of the bad behaviors, and learn the controlrules of the manufacturing units. The behavior recognition device canfurther perform a simulation based on the transition state model.

In some embodiments, a semiconductor manufacturing system includes atleast one semiconductor manufacturing apparatus, and a behaviorrecognition device. The least one semiconductor manufacturing apparatusincludes a series of manufacturing units, a first control unit and afirst storage unit. The series of manufacturing units are configured toperform manufacturing operations on wafers. The first control unit isconfigured to control the manufacturing operations of the series ofmanufacturing units, and generate log data recording the manufacturingoperations of the series of manufacturing units. The first storagedevice is cooperatively connected to the first control unit andconfigured to store the log data transferred from the first controlunit. The behavior recognition device is cooperatively connected to thesemiconductor manufacturing apparatus. The behavior recognition deviceincludes a second storage device and a second control unit. The secondstorage device is cooperatively connected to the first control unit ofthe semiconductor manufacturing apparatus, and configured to store thelog data transferred from the first control unit. The second controlunit is cooperatively connected to the second storage device, andconfigured to receive the log data from the second storage device andbuild a transition state model to analyze behaviors related to themanufacturing operations of the series of manufacturing units based onthe log data.

In some embodiments, a semiconductor manufacturing method includes thefollowing operations. Inputs of log data recording manufacturingoperations of a series of manufacturing units of at least onesemiconductor manufacturing apparatus performed on a plurality of wafersare received by a storage device. A transition state model is built by acontrol unit based on the log data. The transition state model performsanalyzing behaviors related to wafer transfer sequence and activities ofthe manufacturing operations of the series of manufacturing units basedon the log data, generating behavior attributions for the behaviors, andcapturing a root cause of a bad behavior based on the behaviorattributions.

In some embodiments, a behavior recognition device for recognizingbehaviors of a semiconductor manufacturing apparatus includes a storagedevice, a control unit. The storage device is configured to store logdata of the semiconductor manufacturing apparatus. The control unit iscooperatively connected to the storage device, and configured to build atransition state model based on the log data to analyze behaviorsrelated to wafer transfer sequences and manufacturing operations of aplurality of manufacturing units of the semiconductor manufacturingapparatus.

The foregoing outlines structures of several embodiments so that thoseskilled in the art may better understand the aspects of the presentdisclosure. Those skilled in the art should appreciate that they mayreadily use the present disclosure as a basis for designing or modifyingother processes and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein.Those skilled in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions, andalterations herein without departing from the spirit and scope of thepresent disclosure.

What is claimed is:
 1. A semiconductor manufacturing system, comprising:at least one semiconductor manufacturing apparatus, comprising: a seriesof manufacturing units configured to perform manufacturing operations onwafers; a first control unit configured to control the manufacturingoperations of the series of manufacturing units, and generate log datarecording the manufacturing operations of the series of manufacturingunits; and a first storage device cooperatively connected to the firstcontrol unit and configured to store the log data transferred from thefirst control unit; and a behavior recognition device cooperativelyconnected to the semiconductor manufacturing apparatus, the behaviorrecognition device comprising: a second storage device cooperativelyconnected to the first control unit of the semiconductor manufacturingapparatus, and configured to store the log data transferred from thefirst control unit; and a second control unit cooperatively connected tothe second storage device, and configured to receive the log data fromthe second storage device and build a transition state model to analyzebehaviors related to the manufacturing operations of the series ofmanufacturing units based on the log data.
 2. The semiconductormanufacturing system of claim 1, wherein the series of manufacturingunits comprises heterogeneous manufacturing units configured to performdifferent manufacturing operations,
 3. The semiconductor manufacturingsystem of claim 2, wherein the heterogeneous manufacturing unitscomprises load locks, one or more aligners, processing chambers androbots.
 4. The semiconductor manufacturing system of claim 1, whereinthe first control unit real-time transfers the log data to the secondstorage device of the behavior recognition device and to the firststorage device of the semiconductor manufacturing apparatus.
 5. Thesemiconductor manufacturing system of claim 1, wherein the secondstorage device of the behavior recognition device includes a networkattached storage (NAS).
 6. The semiconductor manufacturing system ofclaim 1, wherein the behavior recognition device further comprises adata transmission unit cooperatively connected to the second storagedevice and the second control unit, and configured to real-time transferthe log data of the semiconductor manufacturing apparatus from thesecond storage device to the second control unit.
 7. The semiconductormanufacturing system of claim 1, wherein the second control unit of thebehavior recognition device comprises a processor configured to processthe log data of the semiconductor manufacturing apparatus.
 8. Thesemiconductor manufacturing system of claim 1, wherein the behaviorrecognition device further comprises a parser configured to convertdifferent formats of the log data of the manufacturing operations of themanufacturing units into a uniform format as an input of the secondcontrol unit.
 9. The semiconductor manufacturing system of claim 1,wherein the behavior recognition device is further configured to performa simulation based on the transition state model.
 10. A semiconductormanufacturing method, comprising: receiving, by a storage device, inputsof log data recording manufacturing operations of a series ofmanufacturing units of at least one semiconductor manufacturingapparatus performed on a plurality of wafers; and building a transitionstate model, by a control unit, based on the log data to: analyzingbehaviors related to wafer transfer sequence and activities of themanufacturing operations of the series of manufacturing units based onthe log data; generating behavior attributions for the behaviors; andcapturing a root cause of a bad behavior based on the behaviorattributions.
 11. The semiconductor manufacturing method of claim 10,further comprising converting, by a parser, different formats of the logdata of the manufacturing operations of the manufacturing units into auniform format as the inputs of the log data.
 12. The semiconductormanufacturing method of claim 10, wherein the transition state modelcomprises a Neural Network model.
 13. The semiconductor manufacturingmethod of claim 10, wherein the transition state model comprises: afirst tier configured to learn the wafer transfer sequences and atransition probability of each. transition in the wafer transfersequences for each of the wafers in the manufacturing units based on thelog data; a second tier configured to learn manufacturing cycles of themanufacturing operations of the manufacturing units and a timeconsumption of each activity in the manufacturing cycles; and a thirdtier configured to analyze behaviors related to the manufacturingoperations of the series of manufacturing units by cross learningbetween the first tier and the second tier.
 14. The semiconductormanufacturing method of claim 13, wherein the first tier is furtherconfigured to identify normal sequences and abnormal sequences for thewafers based on the transition probabilities and/or loops of the wafertransfer sequences.
 5. The semiconductor manufacturing method of claim14, wherein the second tier is further configured to identify normalactivities and abnormal activities based on the time consumption and themanufacturing cycles.
 16. The semiconductor manufacturing method ofclaim 15, wherein the third tier is further configured to identify goodsequences and bad sequences from the normal sequences and the abnormalsequences and the good activities and bad activities from the normalactivities and the abnormal activities in terms of productivity.
 17. Thesemiconductor manufacturing method of claim 10, further comprising:outputting the transition state model to a simulator; reproducing thebehaviors in the simulator; and simulating the manufacturing operationsin the transition state model by adjusting control rules of themanufacturing units.
 18. The semiconductor manufacturing method of claim17, further comprising performing the manufacturing operations of theseries of manufacturing units on a plurality of wafers with the adjustedcontrol rule based on a result of the simulation.
 19. A behaviorrecognition device for recognizing behaviors of a semiconductormanufacturing apparatus, comprising: a storage device configured tostore log data of the semiconductor manufacturing apparatus; a controlunit cooperatively connected to the storage device, and configured tobuild a transition state model based on the log data to analyzebehaviors related to wafer transfer sequences and manufacturingoperations of a plurality of manufacturing units of the semiconductormanufacturing apparatus.
 20. The behavior recognition device of claim19, wherein the transition state model comprises: a first tierconfigured to learn wafer transfer sequences and a transitionprobability of each transition in the wafer transfer sequences for eachwafer in the manufacturing units based on the log data; a second tierconfigured to learn manufacturing cycles of the manufacturing operationsof the manufacturing units and a time consumption of each activity inthe manufacturing cycles; and a third tier configured to analyzebehaviors related to the manufacturing operations of the series ofmanufacturing units by cross learning between the first tier and thesecond tier.